Transient Prediction of Nanoparticle-Laden Droplet Drying Patterns through Dynamic Mode Decomposition.

Nanoparticle-laden sessile droplet drying has a wide impact on applications. However, the complexity affected by the droplet evaporation dynamics and particle self-assembly behavior leads to challenges in the accurate prediction of the drying patterns. We initiate a data-driven machine learning algorithm by using a single data collection point via a top-view camera to predict the transient drying patterns of aluminum oxide (AlO) nanoparticle-laden sessile droplets with three cases according to particle sizes of 5 and 40 nm and AlO concentrations of 0.